






"""simple docstring"""





def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int  )    ->							int:
						while b:
												__magic_name__,			__magic_name__:	Optional[int]							=       b, a % b
						return a



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int  )    ->							int:
						return a if b == 0 else euclidean_gcd_recursive(__UpperCAmelCase      ,   a % b  )



def   a    (     )    ->							List[Any]:
						print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3      ,   5  )}'  )
						print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5      ,   3  )}'  )
						print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1      ,   3  )}'  )
						print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3      ,   6  )}'  )
						print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6      ,   3  )}'  )

						print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3      ,   5  )}'  )
						print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5      ,   3  )}'  )
						print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1      ,   3  )}'  )
						print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3      ,   6  )}'  )
						print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6      ,   3  )}'  )


if __name__ == "__main__":
							main()







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




